Deep Learning : What & Why ? – codeburst

“Deep Learning Algo uses :Multiple layers of Non-linear processing unit where each successive layer uses the output from previous layer as an input.A Supervised Learning for Classification Unsupervised for Pattern AnalysisUse some form of gradient descent for trainingDue to multiple level of data processing it forms hierarchical data representation where higher level learns from lower level data layers.Img src : blogs.nvidia.comOrigin of Deep Learning :In 1986 Rina Dechter coined the expression Deep Learning for the first time, but Ivakhnenko Lapa in 1965 wrote the first working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Ivakhnenko and Lapa in 1965.

Recurrent Neural Network :RNN is a type of artificial neural network(ANN) where each connections between machine units forms a directional circle.When back-propagation was first introduced, its most exciting use was for training recurrent neural networks (RNNs).

When we consider the outputs of the hidden units at different discrete time steps as if they were the outputs of different neurons in a deep multilayer network, it becomes clear how we can apply backpropagation to train RNNs.

The key differentiator is feedback within the network, which could manifest itself from a hidden layer, the output layer, or some combination thereof.RNN : Image src : ibm.comNext we will go more deeper into Deep learning techniques like RNN, CNN, DBN etc in details and see how they works with some practical examples and also understand advantages of deep learning techniques .

This whole technology of putting artificial brain into machine is really fascinating , we humans because of our ingenious intellect has this natural instincts to go beyond what seems impossible to… @andi_staub: #DeepLearning: What & Why ?

#AI #ML #DataScience #fintech #Insurtech

This whole technology of putting artificial brain into machine is really fascinating , we humans because of our ingenious intellect has this natural instincts to go beyond what seems impossible to create tools and technology which becomes an extension to our day to day life, which can make decisions on our part and make our living super efficient. It is with this urge to make humans super productive we started putting artificial intelligence to the computer machines and now we have come a long way to build machines which can nearly think like human brains.

Today we will see how Deep learning a branch of ML is really doing justice to all those valuable data floating around in this universe and processing it efficiently to help us reach to some rational conclusions in the filed of Speech recognition, Image Recognition , NLP , Healthcare , Financial Sector etc..

As we know that various Machine Learning techniques has been used to process our raw data to help us is content filtering on social network , to write recommendation engine for e-commerce based portals, In Image and Pattern recognitions, to transcribe speech to text etc. Most of these task are being implemented using a most popular class of ML called Deep Learning

“ Deep learning is a class of machine learning algorithms that allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. “

In 1986 Rina Dechter coined the expression Deep Learning for the first time, but Ivakhnenko & Lapa in 1965 wrote the first working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Ivakhnenko and Lapa in 1965.These ideas were implemented in a computer identification system by the World School Council London called “Alpha”, which demonstrated the learning process.

To understand how deep learning works we need to understand its architecture in depth.

The mother art is architecture. Without an architecture of our own we have no soul of our own civilisation

There are generally 5 most popular deep learning network architecture :

RNN is a type of artificial neural network(ANN) where each connections between machine units forms a directional circle.

When back-propagation was first introduced, its most exciting use was for training recurrent neural networks (RNNs). Juergen Schmidhuber: a leading researcher in ML gave an awesome explanations of RNN :

Recurring neural network allow for both parallel and sequential computation, and in principle can compute anything a traditional computer can compute. Unlike traditional computers, however, Recurrent Neural Networks are similar to the human brain, which is a large feedback network of connected neurons that somehow can learn to translate a lifelong sensory input stream into a sequence of useful motor outputs. The brain is a remarkable role model as it can solve many problems current machines cannot yet solve.

For tasks that involve sequential inputs, such as speech and language, it is often better to use RNNs . RNNs process an input sequence one element at a time, maintaining in their hidden units a ‘state vector’ that implicitly contains information about the history of all the past elements of the sequence. When we consider the outputs of the hidden units at different discrete time steps as if they were the outputs of different neurons in a deep multilayer network, it becomes clear how we can apply backpropagation to train RNNs. RNNs are very powerful dynamic systems, but training them has been a bit challenging because backpropagated gradients either grow or shrink at each time step, so over many time steps they typically explode or vanish.

Fully recurrent : Basic RNNs are a network of neuron-like nodes, each with a directed (one-way) connection to every other node.Each node (neuron) has a time-varying real-valued activation. Each connection (synapse) has a modifiable real-valued weight. Nodes are either input (receiving data from outside the network), output nodes (yielding results) or hidden nodes (that modify the data en route from input to output).

Recursive : Here RNN is created by applying same set of weights recursively over a differentiable graph-like structure, by traversing the structure in topological order. A special case of recursive neural networks is the RNN whose structure corresponds to a linear chain. Recursive neural networks have been applied to natural language processing. The Recursive Neural Tensor Network uses a tensor-based composition function for all nodes in the tree.

RNNs consist of a rich set of architectures (LSTM is one of the popular topology RNN network ). The key differentiator is feedback within the network, which could manifest itself from a hidden layer, the output layer, or some combination thereof.

Next we will go more deeper into Deep learning techniques like RNN, CNN, DBN etc in details and see how they works with some practical examples and also understand advantages of deep learning techniques .

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